futureagi 0.6.9

We help GenAI teams maintain high-accuracy for their Models in production.

Future AGI SDK

The world's most accurate AI evaluation, observability and optimization platform

Documentation | Website | Community

---

🚀 What is Future AGI?

Future AGI empowers GenAI teams to build near-perfect AI applications through comprehensive evaluation, monitoring, and optimization. Our platform provides everything you need to develop, test, and deploy production-ready AI systems with confidence.

✨ Key Features

• 🎯 World-Class Evaluations — Industry-leading evaluation frameworks powered by our Critique AI agent
• ⚡ Ultra-Fast Guardrails — Real-time safety checks with sub-100ms latency
• 📊 Dataset Management — Programmatically create, update, and manage AI training datasets
• 🎨 Prompt Workbench — Version control, A/B testing, and deployment management for prompts
• 📚 Knowledge Base — Intelligent document management and retrieval for RAG applications
• 📈 Advanced Analytics — Deep insights into model performance and behavior
• 🤖 Simulate your AI system — Simulate your AI system with different scenarios and see how it performs
• Add Observability — Add observability to your AI system to monitor its performance and behavior

---

📦 Installation

Python

pip install futureagi

TypeScript/JavaScript

npm install @futureagi/sdk
# or
pnpm add @futureagi/sdk

Requirements: Python >= 3.6 | Node.js >= 14

---

🔑 Authentication

Get your API credentials from the Future AGI Dashboard:

export FI_API_KEY="your_api_key"
export FI_SECRET_KEY="your_secret_key"

Or set them programmatically:

import os
os.environ["FI_API_KEY"] = "your_api_key"
os.environ["FI_SECRET_KEY"] = "your_secret_key"
os.environ["FI_BASE_URL"] = "https://api.futureagi.com"

---

🎯 Quick Start

📊 Dataset Management

Create and manage datasets with built-in evaluations:

from fi.datasets import Dataset
from fi.datasets.types import (
    Cell, Column, DatasetConfig, DataTypeChoices,
    ModelTypes, Row, SourceChoices
)

# Create a new dataset
config = DatasetConfig(name="qa_dataset", model_type=ModelTypes.GENERATIVE_LLM)
dataset = Dataset(dataset_config=config)
dataset = dataset.create()

# Define columns
columns = [
    Column(name="user_query", data_type=DataTypeChoices.TEXT, source=SourceChoices.OTHERS),
    Column(name="ai_response", data_type=DataTypeChoices.TEXT, source=SourceChoices.OTHERS),
    Column(name="quality_score", data_type=DataTypeChoices.INTEGER, source=SourceChoices.OTHERS),
]

# Add data
rows = [
    Row(order=1, cells=[
        Cell(column_name="user_query", value="What is machine learning?"),
        Cell(column_name="ai_response", value="Machine learning is a subset of AI..."),
        Cell(column_name="quality_score", value=9),
    ]),
    Row(order=2, cells=[
        Cell(column_name="user_query", value="Explain quantum computing"),
        Cell(column_name="ai_response", value="Quantum computing uses quantum bits..."),
        Cell(column_name="quality_score", value=8),
    ]),
]

# Push data and run evaluations
dataset = dataset.add_columns(columns=columns)
dataset = dataset.add_rows(rows=rows)

# Add automated evaluation
dataset.add_evaluation(
    name="factual_accuracy",
    eval_template="is_factually_consistent",
    required_keys_to_column_names={
        "input": "user_query",
        "output": "ai_response",
        "context": "user_query",
    },
    run=True
)

print("✓ Dataset created with automated evaluations")

🎨 Prompt Workbench

Version control and A/B test your prompts:

from fi.prompt import Prompt, PromptTemplate, ModelConfig, MessageBase

# Create a versioned prompt template
template = PromptTemplate(
    name="customer_support",
    messages=[
        {"role": "system", "content": "You are a helpful customer support agent."},
        {"role": "user", "content": "Help {{customer_name}} with {{issue_type}}."},
    ],
    variable_names={"customer_name": ["Alice"], "issue_type": ["billing"]},
    model_configuration=ModelConfig(model_name="gpt-4o-mini", temperature=0.7)
)

# Create and version the template
client = Prompt(template)
await client.open()  # Draft v1
await client.commitCurrentVersion("Initial version", set_as_default=True)

# Assign deployment labels
await client.labels().assign("Production", "v1")

# Compile with variables
compiled = client.compile({"customer_name": "Bob", "issue_type": "refund"})
print(compiled)

A/B Testing Example:

import OpenAI from "openai"
from fi.prompt import Prompt

# Fetch different variants
variant_a = await Prompt.getTemplateByName("customer_support", label="variant-a")
variant_b = await Prompt.getTemplateByName("customer_support", label="variant-b")

# Randomly select and use
import random
selected = random.choice([variant_a, variant_b])
client = Prompt(selected)
compiled = client.compile({"customer_name": "Alice", "issue_type": "refund"})

# Send to your LLM provider
openai = OpenAI(api_key="your_key")
response = openai.chat.completions.create(model="gpt-4o", messages=compiled)

📚 Knowledge Base (RAG)

Manage documents for retrieval-augmented generation:

from fi.kb import KnowledgeBase

# Initialize client
kb_client = KnowledgeBase(
    fi_api_key="your_api_key",
    fi_secret_key="your_secret_key"
)

# Create a knowledge base with documents
kb = kb_client.create_kb(
    name="product_docs",
    file_paths=["manual.pdf", "faq.txt", "guide.md"]
)

print(f"✓ Knowledge base created: {kb.kb.name}")
print(f"  Files uploaded: {len(kb.kb.file_names)}")

# Update with more files
updated_kb = kb_client.update_kb(
    kb_id=kb.kb.id,
    file_paths=["updates.pdf"]
)

# Delete specific files
kb_client.delete_files_from_kb(file_ids=["file_id_here"])

# Clean up
kb_client.delete_kb(kb_ids=[kb.kb.id])

---

🎯 Core Use Cases

Feature	Use Case	Benefit
Datasets	Store and version training/test data	Reproducible experiments, automated evaluations
Prompt Workbench	Version control for prompts	A/B testing, deployment management, rollback
Knowledge Base	RAG document management	Intelligent retrieval, document versioning
Evaluations	Automated quality checks	No human-in-the-loop, 100% configurable
Guardrails	Real-time safety filters	Sub-100ms latency, production-ready

---

📚 Documentation

• Complete Documentation
• Dataset SDK Guide
• Prompt Workbench Guide
• Knowledge Base Guide
• API Reference

---

🤝 Language Support

Language	Package	Status
Python	futureagi	✅ Full Support
TypeScript/JavaScript	@futureagi/sdk	✅ Full Support
REST API	cURL/HTTP	✅ Available

---

🆘 Support & Community

• 📧 Email: [e-mail]
• 💼 LinkedIn: Future AGI Company
• 🐦 X (Twitter): @FutureAGI_
• 📰 Substack: Future AGI Blog

---

💡 Why Future AGI?

🤖 Human-Free Evaluations

Our Critique AI agent delivers powerful evaluations without human-in-the-loop. It's 100% configurable for any use case — if you can imagine it, you can evaluate it.

🔒 Privacy First

Don't want to share data? Install our SDK in your private cloud and get all the benefits while keeping your data secure.

🎨 Multimodal Support

Work with text, images, audio, video, or any data type. Our platform is truly data-agnostic.

⚡ 2-Minute Integration

Just a few lines of code and your data starts flowing. No complex setup, no lengthy onboarding.

---

📄 License

This project is licensed under the MIT License - see the LICENSE.md file for details.

---

Get Started Now | View Documentation

Made with ❤️ by the Future AGI Team